In order to navigate huge document collections efficiently, tagged hierarchical structures can be used. For users, it is important to correctly interpret tag combinations. In this ...
We combine linear discriminant analysis (LDA) and K-means clustering into a coherent framework to adaptively select the most discriminative subspace. We use K-means clustering to ...
The recovery of software architecture is a first important step towards re-engineering a software system. Architecture recovery usually involves clustering. The problem with curre...
The ability to classify packets according to pre-defined rules is critical to providing many sophisticated value-added services, such as security, QoS, load balancing, traffic acco...
We initiate a novel study of clustering problems. Rather than specifying an explicit objective function to optimize, our framework allows the user of clustering algorithm to speci...